The field of the invention is systems and methods for magnetic resonance imaging (MRI″). More particularly, the invention relates to systems and methods for motion correction of magnetic resonance images.
Due to the relatively long scans times in MRI, patient motion often results in image artifacts. When imaging some structures, such as the brain, the patient's motion can be approximated as rigid body motion with three degrees of both rotation and translation applied to the entire image volume in a time-varying manner. The PROPELLER method, described by J. G. Pipe in “Motion correction with PROPELLER MRI: application to head motion and free-breathing cardiac imaging,” Magn Reson Med, 1999; 42(5):963-969, has been developed to estimate and remove the effects of in-plane rigid body motion, while mitigating the effects of through-plane motions via the use of data rejection.
The PROPELLER method for motion correction includes both data acquisition and image reconstruction. Data are acquired in concentric rectangular strips, or “blades,” that are rotated about the k-space origin. The central region of k-space is sampled with every blade, which allows for the correction of spatial inconsistencies in position, rotation, and phase between blades. Sampling the central region of k-space with each blade also allows for the rejection of data based on a correlation measurement that indicate through-plane motion, and decreases motion artifacts by effectively averaging low spatial frequencies.
PROPELLER has been shown to be quite effective at mitigating patient motion, but it is not error-free. Inaccurate estimates of motion can lead to corruption of otherwise motion-free data sets, as well as result in non-optimal correction of motion-corrupted data sets. The motion estimates determined in the PROPELLER method are obtained separately for rotation and translation, but in both cases the motion is framed as a simple shift estimate. For instance, rotation correction is performed in the theta direction after gridding the k-space data to polar coordinates, and translation correction is performed directly in x-y space.
The PROPELLER algorithm estimates the aforementioned shifts by maximizing data correlation between each blade and a reference blade. Two components of this shift estimation algorithm can be potential sources for the remaining errors in this estimation. First, the PROPELLER algorithm depends on choosing an appropriate “reference” blade, to which other blades are iteratively aligned. Poor choice of the reference blade can result a non-optimal solution. A second potential source of error is that when choosing the shift that gives the maximum correlation between blades, the precision of this fit with regard to system noise is not addressed.
It would therefore be desirable to provide systems and methods for motion correction in MRI that address the aforementioned drawbacks of the PROPELLER method.